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Leading Edge Commentary
A Diagnosis for All Rare Genetic Diseases: The Horizon and the Next Frontiers
Kym M. Boycott1*, Taila Hartley1, Leslie G. Biesecker2, Richard A. Gibbs3, A. Micheil Innes4, Olaf Riess5, John Belmont62, Sally L. Dunwoodie7, Nebojsa Jojic8, Timo Lassmann9, Deborah Mackay10, I. Karen Temple10, Axel Visel11, Gareth Baynam12
1. Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada.
2. Medical Genomics and Metabolic Genetics Branch, National Human Genome Research institute, Bethesda, MD, USA
3. Human Genome Sequencing Center, Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX, USA
4. Department of Medical Genetics and Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
5. Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
6. Illumina, Madison, WI, USA7. Victor Chang Cardiac Research Institute, Faculty of Medicine, University of New South
Wales, Sydney, NSW, Australia8. Microsoft Research, Seattle, Washington, USA9. Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia10. Department of Human Genetics and Genomic Medicine, Faculty of Medicine, University of
Southampton, Southampton, Hampshire, UK11. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National
Laboratory, the DOE Joint Genome Institute, and the University of California at Merced, CA, USA
12. Faculty of Health and Medical Sciences, University of Western Australia Medical School and Western Australian Register of Developmental Anomalies, Genetic Services of Western Australia and the Office of Population Health Genomics, Western Australian Department of Health, Perth, WA, Australia
*Correspondence: [email protected]
Submission: Cell Special Review IssueTheme: Human Genetics and GenomicsWord count: 3062Tables: 1Figures: 1References: 15
1
The introduction of exome sequencing in the clinic has sparked tremendous optimism for the
future of rare disease diagnosis and there is exciting opportunity to further leverage these
advances. To provide diagnostic clarity to all of these patients, however, there is a critical need
for the field to develop and implement strategies to understand the mechanisms underlying all
rare diseases and translate these to clinical care.
2
INTRODUCTION
Hundreds of millions of lives are affected by an estimated 10,000 unique genetically-
determined diseases. Individually each disease affects a relatively small number of people,
leading to their common label as rare genetic diseases (RDs); however, collectively they
represent an important public health opportunity. The vast majority of these patients
experience long and grueling diagnostic odysseys and lack treatment. In 2011, recognition of
both the longstanding inequity in care and the great opportunity for tractability due to
technical developments led to the founding of the International Rare Diseases Research
Consortium (IRDiRC), which aims to advance global cooperation amongst numerous
stakeholders (Dawkins et al., 2018). The vision of IRDiRC is to enable all people living with a RD
to receive an accurate diagnosis, care, and available therapy within 1 year of coming to medical
attention (Austin et al., 2018). Achieving an accurate and timely molecular diagnosis will largely
depend on progress in the discovery of the genes and genetic mechanisms associated with RDs.
While the exact number of RDs is debated (Hartley et al., 2018), it is estimated that thousands
of RD genes and disease mechanisms remain undiscovered. Over the past eight years, exome
sequencing (ES) in both research and clinical settings has been a powerful tool for discovering
new disease genes for RDs that were intractable to previous approaches. Most advances have
been for highly recognizable clinical presentations associated with early age of onset and
significant morbidity and mortality, and caused by highly penetrant (typically protein-coding)
variants (Boycott et al., 2017). The diagnostic utility of ES has translated beautifully into the
clinic, with a diagnostic yield in the range of 25-30% amongst large and heterogeneous RD
cohorts (Clark et al., 2018). Here, we discuss the continued importance of ES in both the clinic
and the research environment, the next wave of technologies on the horizon, and the next
frontiers for RD discovery, moving towards the ultimate goal of diagnostic clarity for each and
every family affected by a RD.
3
ACHIEVING A DIAGNOSIS FOR ALL
The ‘here and now’: The continued role of exome sequencing
The application of ES with RD patients represents a remarkable achievement in diagnostics with
a diagnostic yield far higher than other genetic tests (Clark et al., 2018). Nonetheless, in >70%
of patients in whom there was a high degree of pre-test suspicion for a monogenic RD, ES
provides no molecular diagnosis. For the benefit of RD patients, it is imperative that we drive
this diagnostic yield to as close to 100% as possible. While the theoretical yield of ES is
unknown, in patient populations with specific presentations and a high degree of certainty that
there is a genetic cause to the RD, the yield of the coding genome is likely well over 50%
(Boycott et. al., 2014; Shamseldin et al., 2017). Indeed, there remains substantial diagnostic
potential in existing ES data. For starters, evidence is emerging that re-analysis of negative
clinical ES data just one to three years later increases diagnostic yield by 10% (Wenger et al.,
2017). This is because at initial analysis there was insufficient evidence for candidate variant or
gene causality, but this evidence emerges upon reanalysis in light of the annual curation of
>10,000 disease variants [ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/ ) and HGMD
(http://www.hgmd.cf.ac.uk/ac/index.php)] and 250 novel disease-gene associations [OMIM
(https://www.omim.org/ ) and Orphanet (https://www.orpha.net/consor/cgi-bin/index.php)].
Even higher diagnostic yields can be achieved through reanalysis in collaboration with the
referring physician, with estimates as high as 12% (Salmon et al., 2018). Collaboration with
research laboratories can provide additional increases (Eldomery et al., 2017), boosted by the
application of novel computational tools, sequencing of additional family members, and gene
discovery efforts. These strategies have been bolstered by platforms that share genotype and
phenotype information to identify patients with overlapping phenotypes and candidate genes,
an approach called matchmaking (reviewed in Philippakis et al. 2015;
www.matchmakerexchange.org). While technical limitations of ES are well-recognized, in the
last decade capture kits have continued to improve their coverage of the coding genome, with
additional gene features such as promoters and intron flanking regions. In addition,
computational tools continue to improve and facilitate the identification of variation. Given cost
4
and other practical considerations, ES will continue to play a major role in RD variant diagnosis
and discovery.
Globally, thousands of clinical exomes are performed weekly but, unfortunately, the majority of
these data are inaccessible for discovery and matchmaking. To realize the theoretical maximal
diagnostic yield of ES will require a globally coordinated paradigm shift; every patient must
have the opportunity to be a research patient. More international and less restrictive data-
sharing is critical to drive variant interpretation, better control datasets, and development of
computational tools. This will enable identification of RDs that are understood at a genetic
level, while RDs that require further research can be studied as part of an ‘exome negative’
clinical infrastructure. Importantly, aggregation of such data will contribute to the development
of large datasets that can be used for as yet undefined purposes as we explore new
mechanisms for RD.
The ‘horizon’ in RD diagnosis: The next wave of technologies to reveal RD mechanisms
Regardless of the ultimate capability of ES to provide diagnoses for RD patients, some disease
mechanisms are difficult or impossible to detect using this approach (Table 1). For example,
mosaicism of a pathogenic variant would not be routinely identified by current analytical
approaches. Challenges for detection of mosaicism include: the distribution of the causative
genomic variation which can be non-random and can exclude the most often sampled tissue
(blood) for genetic testing; the changing level of mosaicism over time; the difficulty in
distinguishing pathogenic from benign or unrelated mosaic variation (signal-to-noise); and the
high sequencing cost for the depth and breadth of coverage needed to detect low level mosaic
variants. New data analysis tools are emerging to screen for mosaicism in unsolved exome
datasets, and approaches that facilitate very deep sequencing of targeted regions in a cost-
effective manner will improve detection of mosaicism in the near-term.
Some pathogenic genomic variants are missed entirely by ES. Genome sequencing (GS) (short-
read sequencing) out-performs ES for indels (small insertions-deletions), CNVs, (copy number 5
variations) and chromosomal rearrangements, while long-read GS promises further
improvements in detection of rearrangements and the ability to identify RDs secondary to
pathogenic repeat expansions. GS also provides the opportunity to identify regulatory variants
that lie outside the exome, such as in promoters, enhancers, deep intronic regions, or distant-
acting regulatory sequences located in intergenic regions; though interpretation of such
variants and proof of causality are challenging. Such advantages of GS are the basis for
promoting this approach over ES, and while robust head-to-head comparisons of the two
approaches are still lacking, we hypothesize that GS will increase the diagnostic yield of a
genome-wide clinical test by at least 10% in the near-term. As clinical GS data accumulate and
understanding of intronic and intergenic variation improves, this yield will significantly increase
over the years.
Several emerging technologies offer value as adjunct diagnostic tools by providing an approach
to assess the functional significance of variants. For example, transcriptome sequencing can
evaluate the functional consequences of variants that may affect splicing or gene regulation
(e.g., decreased, increased, or monoallelic gene expression). This approach has been suggested
to increase the diagnostic yield by 10-35% in known genes for certain clinical indications.
Although promising, its broad applicability for RDs is unknown given challenges around the
availability of relevant tissues, including those at critical stages of development. Similarly,
methylation arrays are providing functional insight into imprinting disorders, which are caused
by alterations of the expressed copy number of imprinted genes, through epigenetic error,
uniparental disomy, or CNVs/SNVs (single nucleotide variants) of the regulatory DNA or the
expressed allele (Soellner et al., 2017). More than one hundred human germline imprinted
genes distributed across the genome have been identified, and it is likely that more remain to
be found. In addition, arrays can detect specific DNA methylation epi-signatures for RDs
associated with chromatin dysregulation; these syndrome-specific biomarkers complement
standard clinical diagnostics (Aref-Eshghi et al., 2018). The true prevalence and phenotypic
spectra of imprinting disorders will only be determined by the coordinated implementation of
genomic and epigenomic technologies and recognition that the right family member to analyze 6
might not be the affected individual. Similarly, atypical inheritance patterns should be
considered when analyzing genomic data of unsolved RD patients, this will require even more
sophisticated approaches to data analysis that will identify such mechanisms in a diagnostic
setting (Table 1). Finally, for all of these new technologies, diagnostic standards will need to be
established before clinical implementation to facilitate diagnostic clarity for as many patients as
possible.
The ‘next frontiers’ in RD discovery: Building out from Mendelian inheritance
Despite the excitement around GS, few RD discoveries have been made outside of the protein-
coding regions of the genome. Comprehensive analysis of the noncoding genome on a broader
scale represents a significant frontier (Table 1). The opportunity lies in the interpretation of
noncoding variation, which is exponentially more difficult given the unresolved complexity of
how noncoding DNA regulates gene expression, lack of adequate control datasets, and
computational tools to predict variant impact, and the fact that each of these noncoding
variants is likely affecting only a single patient or family resulting in a high benchmark to
establish pathogenicity. While confirming predicted splicing abnormalities is relatively
straightforward as highlighted in the previous section, estimating the impact of mechanisms
such as long-range DNA regulation, aberrant DNA modifications (such as methylation),
pathogenic alterations to non-coding RNA, and post-transcriptional and post-translational
dysregulation on RD will require significantly greater understanding of the genome and
significant development of functional analytical approaches. Initial successes will likely center
on the use of large families with linkage data to narrow the search space and a focus on
noncoding de novo alterations in parent-affected child studies.
Alongside monogenic RDs, we will face the challenge of RDs of complex etiology, with a primary
genetic driver but clinical presentations that are contextualized by additional factors and
represents another significant frontier of study. The relative impact of genetic and
environmental components on RDs will depend on the underlying mechanism of interaction
(signal transduction pathway, unfolded protein response, epigenetic modifications, etc.) and in 7
the case of embryogenesis, when during development the impact is elicited and which
developing organs/tissues are most vulnerable to perturbation at a given time. Environmental
exposures may be pre- or post-natal, and the challenge will be to capture such exposure
information based on history as well as the data from the metabolome, the latter of which is
dynamic. These studies require integration of epidemiological and genomic data in exposed and
non-exposed populations. Experimental support for such complex mechanisms will require the
use of functional assays and model organisms to validate findings (Shi et al., 2017).
By comparison, the investigation of digenic, oligogenic, and polygenic inheritance models may
seem relatively straightforward, but one should not be deceived and this represents yet
another frontier. To perform such analyses and collect the evidence required for the statistical
certainty needed to support an RD mechanism, a massive amount of harmonized phenotypic,
genotypic, and family history data will be required. The establishment of such datasets
reinforces the need to offer research access and broad data sharing to all RD patients and their
families.
THE UNSOLVED RD COHORT: THE WAY FORWARD
Our ability to diagnose all RDs is limited by our incomplete understanding of the full mutational
spectrum associated with all RDs and the sheer number of unique RDs that have yet to be
defined. The way forward is readily recognized as multi-faceted and will likely focus on specific
subsets of patients from the unsolved RD cohort (Figure 1); each subset has significant utility for
exploring RD mechanisms and optimizing approaches for clinical translation of novel diagnostic
tests. Patients in the unsolved RD cohort can be considered in four groups and while the
approaches used to uncover the genetic mechanism for the respective RDs may be similar
between groups, the knowledge gained for each will be unique.
Patients with no causative variant after an appropriate, highly sensitive test
Patients in this group have had the appropriate genetic test that is highly sensitive for that
particular RD, but remain without a molecular diagnosis (e.g., single gene disorders such as 8
cystic fibrosis and neurofibromatosis type 1). In all likelihood, the causative variant(s) in these
patients is/are not detected by the current testing methodologies, and therefore this subset of
patients represent a remarkable opportunity to explore novel diagnostic approaches, including
new technologies and computational tools, to more comprehensively assess the spectrum of
possible genetic causes of a given disease. The insights delivered will be directly relevant to
these patients, whilst also optimizing patient sampling, computational tools, and diagnostic
algorithms based on emerging technologies. More broadly, such knowledge will contribute
significantly to the mechanistic spectrum of other unsolved RDs. This type of exploratory focus
represents a shift in the types of studies that our community ‘values’ and both funders and
publishers will need to recognize the intermediate importance and long-term impacts of the
resulting insights.
Patients with no identified causative variant in the context of genetic heterogeneity
Patients in this group have a clinically recognizable presentation associated with genetic
heterogeneity (e.g., hereditary spastic paraplegia, myopathy, and retinitis pigmentosa) but
negative results for the appropriate testing and analysis, including most of the relevant disease
genes. These patients have either a pathogenic variant in one of the known disease genes that
was not detected using the current testing approach, or a yet-to-be-discovered disease-
associated gene. To diagnose these patients, we will need large datasets of patients that
include detailed phenotypic and genomic information for data comparison and novel
technologies and computational tools to identify cryptic variants. Besides GS, transcriptomic,
metabolomic, epigenomic, and proteomic data may be necessary to identify the underlying
genetic causes, particularly for simplex patients in which family-based analyses are not possible.
This patient group represents an opportunity similar to the subset described above but is also
enriched for novel disease gene discovery and likely represents one of the largest populations
in the unsolved cohort.
9
Patients with an unsolved but recognizable syndrome
Patients in this group have a clinical diagnosis based on similarity to a previously described
syndrome for which the underlying etiology is unknown (e.g., PHACE and Hallermann-Streiff
syndromes, and VACTERL association). With the efforts of the clinical and scientific
communities and the increased use of ES, we now understand the genetic basis of most of the
frequent and recognizable human malformation syndromes. However, some well-established
syndromes (defined as reported in >10 unrelated patients and curated by OMIM and Orphanet)
remain without an understood molecular etiology despite intensive investigation. Examples of
such unsolved syndromes have been recently reviewed (Boycott et. al., 2018) in a Special issue
on Unsolved Recognizable Patterns of Human Malformation: Challenges and Opportunities in
the American Journal of Medical Genetics (https://onlinelibrary.wiley.com/journal/15524876).
Possible explanations for their current intractability include genetic and phenotypic
heterogeneity, mosaicism, epigenetics, gene-environment interactions, and other non-
Mendelian contributions. The way forward for this group of disorders will require the use of
emerging and new technologies, global cooperation, and data sharing.
Patients with syndromes without a name
The fourth group of patients present with a constellation of clinical symptoms and signs that
are not recognizable as a previously described syndrome or condition, which may have non-
specific clinical features, and fit into none of the above groups. Part of the challenge in the
diagnosis of these patients is that the full extent of the clinical presentation may not yet have
become manifest (as occurs in the evaluation of ill newborns). These patients are most suitable
for genome-wide sequencing approaches and, for the foreseeable future, should undergo ES or
GS as a first line test followed by detailed genotypic and phenotypic data-sharing for
matchmaking purposes. Their diagnoses will likely include early-presentations of recognized
RDs, expanded phenotypes of previously recognized RDs, and novel RDs associated with new
genes that will only be identified once RD datasets contain sufficient genotypic and phenotypic
data to provide statistical confidence that an accurate diagnosis has been made and/or
following validation in model organisms. 10
CONCLUSIONS
We face a grand opportunity in precision public health; to understand the cause of each and
every RD and provide a diagnosis for each individual RD patient. Clinical ES is transforming
molecular diagnosis and will continue to have a remarkable impact on this area of medicine. For
the patients that remain unsolved after genetic testing, the future remains optimistic. A large
number of emerging technologies are on the horizon and will play an important role in RD
diagnostics in the near-term. Computational approaches that focus on large-scale data
integration across patients and within the single patient (“systems diagnostics”), and from
healthy individuals, will enable the next frontiers of RD discovery. As we work toward our goal
of diagnostic clarity for all, we will gain important insights into the RD genome and the
attendant knowledge about human biology that this will bring. Importantly, there are some
cross-cutting requisites for the clinical and research community to enable this important work
and reach not just the current horizons of RD diagnostics, but the next frontiers as well. To
start, we need to provide all RD patients the ability to access clinical genome-wide testing and
participate in research. At the health systems level, we must implement the timely, prioritized,
and sustainable clinical integration of proven innovative diagnostic approaches; this will scale
the input side of the equation, serving patient need and fueling translational research
discovery. In facilitating research participation, we must include those that we do not typically
consider, collecting data for those with molecular diagnoses, and the clinically diagnosed but
causative-variant negative patients, and support the necessary infrastructure. Going forward,
we need to address the fundamental lack of RD researchers that study complex mechanisms by
enabling an emerging new generation of scientists in this area with adequate funding and by
contributing to comprehensive RD datasets that will provide the foundation for their work.
Most critically, we must recognize that the future of RD diagnostics will depend on the
international RD community working as one team towards an ambitious and important joint
goal. We need to overcome a mindset limited to individual patients, individual researchers,
individual genetic mechanisms, and even individual consortia, countries, or continents. The
11
vision of IRDiRC, for each RD patient to receive a diagnosis within 1 year, is achievable only if we
collectively take up this grand opportunity on a global scale.
ACKNOWLEDGEMENTS
We thank the IRDiRC Scientific Secretariat for their support of the ‘Solving the Unsolved’ Task
Force, funded under the European FP7 contract, “SUPPORT-IRDIRC” (305207). We also thank
Matthew Hurles and Daniel MacArthur for helpful discussions, Lilian Lau and Anneliene Jonker
for their contributions to the Task Force, and Grace Ediae for support with preparation of this
manuscript.
L.G.B. was supported by the intramural research program of the National Human Genome
Research Institute. A.V. was supported by U.S. National Institutes of Health grants
R24HL123879, UM1HL098166, UM1HG009421 and U01DE024427. Work at Lawrence Berkeley
National Laboratory was performed under U.S. Department of Energy Contract DE-AC02-
05CH11231, University of California. G.B. was supported by the Angela Wright Bennett
Foundation, the McCusker Charitable Foundation, and the Roy Hill Community Foundation. T.H.
was supported by a Frederick Banting & Charles Best Canada Graduate Scholarship – Doctoral
Award from CIHR. K.M.B. was supported by a Genome Canada and Ontario Genomics Institute
LSARP Grant (OGI-0147) and CIHR Foundation Grant (FDN-154279).
The opinions expressed here are those of the authors and may not necessarily reflect the
positions or policies of the institutions to which they are affiliated.
CONFLICTS OF INTEREST
J.W.B. is a full time employee of Illumina, Inc. L.G.B. is a member of the Illumina Medical Ethics
committee, receives royalties from Genentech, and receives honoraria from Cold Spring Harbor
Press. The remaining authors have no conflict of interest to declare.
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Hartley, T., Balcı, T. Ber., Rojas, S. K., Eaton, A., Care4Rare Canada Consortium, Dyment, D.A., and Boycott, K.M. (2018). The Unsolved Rare Genetic Disease Atlas? An Analysis of the Unexplained Phenotypic Descriptions in OMIM®. Am. J Med. Genet. Part C 178(4), 458–463.
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Shi, H., Enriquez, A., Rapadas, M., Martin, E. M. M. A., Wang, R., Moreau, J., Lim, C.K., Szot, J.O., Ip, E., Hughes, J.N., et al. (2017). NAD Deficiency, Congenital Malformations, and Niacin Supplementation. N. Engl. J. Med., 377(6), 544–552.
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Figure 1. Clinical Groupings of the Unsolved RD Cohort.
The unsolved cohort of patients can be considered in four groups, each will require a multi-
faceted approach and will give us different insights into the incredible landscape of mechanisms
underlying RD.
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Table 1. Mechanisms of RD that are currently intractable to exome sequencing and analysisMechanism
Description ApproachesGeneral Mechanism
Perspective for Solutions*
Mechanism Subcategory
Mosaicism Horizon Tissue-specific mosaicism
Mosaic manifestations of Mendelian disorders; Disorders that manifest only as mosaicism
Deep sequencing of multiple tissues
Genomic alterations
Horizon Small insertions/deletions
Small structural changes missed by ES and microarray (<50bp)
GS
Large insertions/deletions
Larger structural changes missed by ES and microarray (>50bp)
GS
Chromosomal rearrangements
Inversions/translocations; Multiple deletions/duplications
GS
Repeat expansions Triplet and other expansions Long-read GS
Transposable Elements (Retrotransposons)
Genomic sequences that copy and paste into locations throughout the genome (such as Mobile Element Insertions)
Novel approaches to data analysis
Gene regulation
Horizon Splicing mutations Synonymous or splice site or intronic mutations
GS, RNASeq
Imprinting Altered parent-of-origin specific expression pattern
Methylation arrays
Next Frontier Regulatory DNA mutations
Promoter, enhancer, and other regulatory mutations
GS, RNASeq, High-C, Prediction Tools
Non-coding RNA mutations
Intronic, intergenic, and antisense RNAs (e.g., microRNAs, snoRNAs)
Novel approaches to data analysis
Mutations that alter post-transcriptional or post-translational modifications
Altered RNA or protein modifications that impact stability or catalytic function
Novel approaches to data analysis
Complex inheritance
Horizon Unusual or less common inheritance patterns
Sex-limited expression; paradoxical inheritance; necessary but not sufficient CNVs; uniparental disomy
Novel approaches to data analysis
Next Frontier Genetic modifiers Allele from one gene reduces or exacerbates the penetrance or expressivity of phenotype associated with another gene
Novel approaches to data analysis; Validation in model organisms
Gene-environment interaction
Rare susceptibility allele combined with environmental trigger
Environmental exposure data capture; Validation in model organisms; Metabolomics
Maternal effects Mutation in the mother results in altered fetal environment
Environmental exposure data capture; Validation in model organisms
Digenic, oligogenic, or polygenic
Interaction of two or more genes Novel approaches to data analysis; Validation in model organisms
*Horizon: near-term (within 5 years); Frontier: longer-term (5 years and beyond)
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